Use Cases

This use case focuses on a priority area in news journalism: AI-technology support for detecting disinformation in social media, with a view to support journalistic fact-checking and verification workflows in news organisations.

Journalists are currently working in an extremely challenging environment. The amount of incoming content to work with is ever increasing, and the rise of the platforms has led to the drive to publish as fast as possible to the extreme.

At the same time, journalists need to ensure the published content is both relevant for is audience, and is a trustworthy source of information, avoiding errors and misinformation. Intelligent solutions are needed to help journalists achieving this in more efficient ways.

In the age of smartphones, video sharing platforms and social media, private users tend to generate large amounts of video content.

Those contents may be used by media organisations to complement professional generated content and develop novel revenue paths. There are tools like Automatic Newscast Transcription System, that are efficiently used to search and retrieve information related to previous events inside archives or other professional data sources. However, they lack in providing new, fresh and engaging information as soon as the event occurs.

Moreover, private users can produce and share video content at the click of a button, but breaking news happens unpredictably, and professional news camera crews may not be able to cover event on time. However, many people or news agency correspondents present at the sites of interest have mobile phones and high-speed mobile networks, e.g. 5G, that can contribute potentially valuable material.

This use case facilitates access of cross-border collections and seek examples of search methodologies to provide researchers and journalists with practical methods to sift, connect and analyse various data and media collections in search of factual responses to broad societal research questions.

It links to a fundamental change of the media ecology, which through AI has come in the form of an easy integration of media archives into media productions. News media now present through the same AI interfaces of search, index and access not just the daily news, that has been traditionally their main focus, but also older news. AI has fundamentally changed the relevance over time, the unstructured nature of content and curation and distribution practices in media.

Ever since the birth of the idea of AI, games have been helping AI research progress. Games not only pose interesting and complex problems for AI to solve (e.g., AI that plays a game well) they also offer a canvas for creativity and expression which is experienced by users.

Thus, games define a rare domain where science meets art and interaction: these ingredients have made games a unique and favourite domain for the study of AI. But it is not only AI that is advanced through games; games have also been advanced through AI research in the way we play, understand, design them, and in the way we grasp interaction and creativity.

AI systems are capable of creating artwork, not just by themselves but perhaps more useful as a complex assistant to artists. As an example, in the visual arts, AI contributions range from raw image creation to slight modifications/embellishments through image filters trained using an ML system. In general, audio tools have lagged visual ones, with most tools focused on written music, because of the high data density and throughput needed for raw audio. However, recent techniques have been finally successful in processing and generating high quality raw audio music and voice, usually following an expensive training.

As with visual arts, while some of these creative processes can happen without any human intervention, better results are obtained in a collaborative environment between humans and algorithms. Defining the boundaries of this collaboration brings forward the role played by each actor and the requirements to complete the creative process.

Many of the largest media companies in Europe and around the world have accumulated vast digital archives and collections of images and videos over the years. Since these collections have been gradually and iteratively built over long periods of time, often by different departments and units of media companies, they usually do not have good organisation and often have little metadata such as tags, categories, and other types of annotations.

This lack of coherent media asset organisation, tailored to the media company business and services, precludes the successful monetisation of these media assets and the creation and offering of new services by such companies.